title: Prediction of rat behavior outcomes in memory tasks using functional connections among neurons.
abstract: BACKGROUND
  Analyzing the neuronal organizational structures and studying the changes in the behavior of the organism is key to understanding cognitive functions of the brain. Although some studies have indicated that spatiotemporal firing patterns of neuronal populations have a certain relationship with the behavioral responses, the issues of whether there are any relationships between the functional networks comprised of these cortical neurons and behavioral tasks and whether it is possible to take advantage of these networks to predict correct and incorrect outcomes of single trials of animals are still unresolved.
  
  
  RESULTS
  This paper presents a new method of analyzing the structures of whole-recorded neuronal functional networks (WNFNs) and local neuronal circuit groups (LNCGs). The activity of these neurons was recorded in several rats. The rats performed two different behavioral tasks, the Y-maze task and the U-maze task. Using the results of the assessment of the WNFNs and LNCGs, this paper describes a realization procedure for predicting the behavioral outcomes of single trials. The methodology consists of four main parts: construction of WNFNs from recorded neuronal spike trains, partitioning the WNFNs into the optimal LNCGs using social community analysis, unsupervised clustering of all trials from each dataset into two different clusters, and predicting the behavioral outcomes of single trials. The results show that WNFNs and LNCGs correlate with the behavior of the animal. The U-maze datasets show higher accuracy for unsupervised clustering results than those from the Y-maze task, and these datasets can be used to predict behavioral responses effectively.
  
  
  CONCLUSIONS
  The results of the present study suggest that a methodology proposed in this paper is suitable for analysis of the characteristics of neuronal functional networks and the prediction of rat behavior. These types of structures in cortical ensemble activity may be critical to information representation during the execution of behavior.
authors.affiliation: School of Computer Science, Fudan University, Shanghai, China.
authors.forename: Hu
authors.initials: H
authors.lastname: Lu
authors.forename: Shengtao
authors.initials: S
authors.lastname: Yang
authors.forename: Longnian
authors.initials: L
authors.lastname: Lin
authors.forename: Baoming
authors.initials: B
authors.lastname: Li
authors.forename: Hui
authors.initials: H
authors.lastname: Wei
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001522:Behavior, Animal / Q000502:physiology*; D001921:Brain / Q000166:cytology / Q000502:physiology*; D001931:Brain Mapping; D016000:Cluster Analysis; D003071:Cognition / Q000502:physiology*; D008297:Male; D018782:Maze Learning / Q000502:physiology; D009415:Nerve Net / Q000502:physiology*; D051381:Rats; D017207:Rats, Sprague-Dawley